The core contribution of FedML in the wider context of privacy-preserving machine learning is to enhance input privacy. Compared to alternatives such as homomorphic encryption which allows computation directly on encrypted data [1]}, FedML accommodates a wider range of algorithms. Output privacy deals with the inverse problem of what can be learnt about the input data from making predictions with the resulting global model. Differential privacy (DP) can be used to add controlled noise to carefully chosen stages of the model construction pipeline in order to make it harder to reverse engineer input data [2]}. It is common to combine FedML with privacy enhancing technologies such as HE and DP [3]}, at least with synchronous model update primitives [4]}. Multiparty computation (MPC) can be used for secure aggregation of models to minimize risk for information leakage [5]}.
A comprehensive overview of the current state-of-the art in FedML along with a survey of privacy-enhancing technologies and security is provided by Kairouz et al. [4]}.
